Economists have predicted that a leisurely 15-hour workweek awaits us in the future, with robots taking over the mundane tasks so that we’ll be free to explore the more interesting aspects of our jobs.
One reason we find ourselves in disbelief at the idea of a leisurely (or stimulating) workweek is that the reality of our work is in stark contrast with these futuristic predictions.
McKinsey found that over 40 percent of employees claim to spend at least a quarter of their week on manual, repetitive tasks.
It seems that robots haven’t freed us (yet!).
But there is a silver lining. The process of data automation is a tried-and-tested solution for modern data scientists. And although it might not solve all of your future work problems, it can help you manage data in more efficient and profitable way.
In this blog, you are going to discover how to automate:
Machine learning pipelines
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With Keboola, you can automate manual data engineering tasks, save time, reduce errors, and improve your data quality.
The process of data automation is similar to business workflow automation - you start with a clear strategy to guide you.
The strategy for automating your data operations has three crucial components:
Identification of problems. Identify the major areas where your business could benefit from automation. Ask yourself: where do your data operatives spend the majority of their time doing manual work? Which aspects of your data operations keep breaking down? Create a list of all the potential processes for optimization.
Prioritization. Use the amount of time wasted as a proxy for how important a process is and how it relates to your business initiatives. The more time consumed by manual labor, the greater the impact of automation will be on the bottom line. Make sure that you also consider how long it would take to automate a process. Quick wins keep everyone’s morale up while the business owners see the value of automation.
Execution. This is technically the hardest part: execution of data strategies. We will explore how to execute three different processes: improving reporting, improving the engineering pipeline, and improving machine-learning processes.
How to automate reporting
Business users spend a lot of time on reporting rather than the actual data analysis part of their workload. That is, report generation represents a massive chunk of data analytics.
This is because data analytics provides business-critical information, which needs to be delivered in timely updates to guarantee maximum impact for decision-makers.
In practice, this often means that the data analyst has to re-run the same SQL queries to generate the dashboards and visualizations, export them, and email them to critical collaborators.
One of the easiest ways to automate reporting is to rely on data automation tools such as Looker, which automate report generation and even send emails with finished reports at pre-specified times.
However, data still needs to be unified and imported into BI tools for them to generate reliable, high-quality results. Here is where Keboola comes in handy. It can help:
Automatically extract data from over 250 sources and deliver it to a data warehouse or any other destination of your choice.
Automate your data cleaning and transformations to save you precious time while keeping data clean and validated.
Integrate with dashboarding softwares such as Looker, PowerBI, Qlik, Tableau and others, so you will always have the latest reports in real-time.
The ETL process is a common data engineering method used to process data from its raw form into one that’s usable.
Depending on the underlying infrastructure and architecture, automating your ETL pipeline can be a breeze or require a high level of expertise. We will show you how to automate ETL pipeline in three different ways: with no-code, with low-code, and with full-code examples.
If you’re considering big data automation, automating the ETL pipeline will have the highest impact on the scalability of the big data technologies that you use.
1. Automate data extraction
The extract phase is the part of the ETL pipeline in which you collect data from its raw sources.
The sources can include other databases, enterprise resource planning (ERP) software, customer relationship management (CRM) software, third-party apps accessed via APIs (one of the most common data sources nowadays), and a myriad of other sources.
There are several best practices when automating data collection and data integration:
Use REST APIs or WebSockets. Either schedule REST API calls at regular intervals to keep your data up to date, or implement a webhook/WebSocket, which updates data in your ETL pipeline whenever new data is generated in the source. The latter is faster and keeps your data fresh, while the former is less expensive because it processes data in batches. You need to decide which of the two options works best for your business demands.
Test for the improbable (but possible) worst-case scenario. Data collection scripts break, and they do so often. The payloads and endpoints of APIs change between updates, data types change, data formats are altered, and service reliability issues are a frequent pain. All of these can cause your system to accidentally import incomplete or corrupted data. You need to be prepared for the improbable worst-case scenario and write tests which prevent data imports for extractions that do not adhere to specifications.
Rely on software to make your life easier. Instead of wasting time and money on writing long scripts, maintaining them, and monitoring them for reliability and validity, deploy software that collects data for you. Vendors who offer data platforms provide software to integrate with your sources and take on the responsibility of the software, leaving you with more time for other tasks. Keboola, for example, offers over 250 extractors for automating data collection from third-party applications. No extra code or maintenance is needed.
2. Automate data transformation
Transformation is the second step of the ETL pipeline. It’s at this point that we clean the incoming data and aggregate it into the desired form for loading into the database.
When automating data transformations, keep in mind these best practices:
Write reusable scripts. Modularize your cleaning code so that the scripts can be used on a variety of datasets. For example, when removing outliers, you could write a helper function to remove them, which is also accessible to other modules over other datasets.
Clean preemptively. Even if your data is shaped in the right manner and doesn’t suffer from certain corruption issues, you should apply cleaning scripts to it nonetheless. For example, run the remove_missing_values.py script on tables which don’t have missing values. Why? Because if missing values do appear, you won’t have to re-clean the table manually or change scripts - the system will do it automatically.
Write diagnostic scripts to save computing power. Cleaning preemptively (the advice above) has the shortcoming of wasting (compute) time and slowing down your system. Instead, you could create a set of diagnostic scripts, which check for missing values and run the scripts only when they find them.
Schedule or trigger your transformation processes. Every piece of advice given so far could still be executed manually. What makes the transform stage fully automated is automated execution. You can either schedule the transformation scripts at regular intervals (batch) or trigger the scripts whenever new data comes in. The trade-off is between longer delays in having clean data vs. higher costs of cleaning data constantly.
Move aggregations to the extract phase. APIs and webhooks allow you to specify the structure of the returned payload in advance. You can use this flexibility to specify the aggregation that you need when collecting data ahead of time. Instead of collecting rows of raw data and aggregating manually, simply collect pre-aggregated data.
3. Automate data loading
Data loading automations guarantee that your representation of data is compliant with the data model specifications (aka schema). Use SQL triggers and constraints to make sure that everything is as specified. SQL triggers and constraints operate on a ‘set and forget’ principle.
Data science and machine learning have been revolutionizing businesses in recent years. The era of "big data" is changing the way businesses work in a lot of ways, from automated speech recognition to computer vision and a lot of other new ideas.
Automation is also a disruptive technology that lets us tap into machine learning’s potential for growth. Unfortunately, not much has been written about automating machine-learning pipelines... until now.
Here are five ideas on how to make your machine-learning pipelines run smoother with automation:
Automate labeling. Automated labeling of categorical data into a set of numerical identifiers is simple. Just use scikit-learn’s LabelEncoder to one-hot encode your target variables. If you move this process into the Transform layer of your ETL pipeline, you can automate data preprocessing for classification tasks.
Automate feature engineering. Feature engineering refers to the process of creating new data features to be used as input for machine-learning algorithms. Feature creation can be automated in the same way that we automate the ETL transformation step. Just create an aggregate or arithmetic composite feature out of your raw features during preprocessing and make that data available to your machine-learning notebooks. What’s more, you could also automate the feature selection aspect of feature engineering. This can be done by using established libraries like scikit-learn’s feature_selection module.
Automate model selection. We evaluate ML models against a set of objective metrics, such as their Accuracy, ROC, F1 score, etc. Those same metrics can be used to automate model selection. We simply prepare a couple of potential models (e.g. Logistic Regression, Support Vector Machine, Naive Bayes Classifier, Random Forest Classifier, etc.), train and evaluate each model against an evaluation score (e.g. Accuracy), and then pick the model that performs best. That model is shipped to production. With little work, we can automate the entire data pipeline for picking the best ML model.
Auto-tune hyperparameters. Machine-learning models are only as good as their configurations. Finding the right set of hyperparameters can boost your model’s accuracy by orders of magnitude. You can automate hyperparameter tuning quite easily. Use GridSearchCV as an exhaustive search strategy to train the same model with different hyperparameters, then pick the best set of hyperparameters and ship them to production. This automation step follows the same logic as the previous one.
Automate predictive analytics. If you followed the advice above, you have all of the necessary means to automate predictive analytics. Simply schedule or trigger a call model.predict() on your fresh data with your newly built model and save the results with a new table. Combine this automated pipeline with reporting, and you’ll have automated everything from ETL to ML to BI.
Get Keboola for fast and easy data automation
Before you start reaping the benefits of data automation, you will need to go through the process of setting it up.
And if you already know that using an automation tool will make your life easier, then look no further than Keboola.
Keboola is a data platform as a service with tools that help you automate your data processes in minutes:
Automatically extract data from multiple data sources without writing a single line of code or worrying about maintenance.
Automate your data cleaning and transformations to save precious time while keeping data clean and validated
Integrate with your dashboarding software so you will always have the latest reports as soon as new data becomes available.
Keboola has a proven track record of helping clients set up their data foundations and build scalable data infrastructure that supports their business goals.
Data automation is the use of intelligent processes, choice of infrastructure, application of artificial intelligence, and software to collect, transform, store, and analyze data without human intervention.
Automated workflows replace time-consuming tasks and speed up data-driven decision-making processes.
What are the benefits of data automation?
Reduction in the time wasted on manual processes gives domain experts the opportunity to shine in what they do best - putting their expertise towards solving problems and generating more revenue.
In essence companies with any volume of data can enjoy 2 benefits from data automation:
Reduces operational costs. Resolving bugs and manually running queries leads to an opportunity cost for everyone involved.
Less error-prone. Data automation results in improved data quality.
What is data analytics automation?
Automated analytics refers to the use of computer systems to deliver analytical products with little or no human intervention.
Automated analytics can be used in a number of ways. For example, you can automate full data processes, automate full business intelligence dashboards, create data-driven self-governing machine learning models, or you can automate singular tasks such as:
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